This course targets quantitative ecologists and students who need to handle complex nonlinear statistical models (both frequentist and Bayesian). AD Model Builder (admb-project.org) is a highly efficient, freely available software for implementing non-linear statistical models. The main reasons for preferring AD Model builder are: (1) Flexibility. The user is free to define any desired model, and not limited to choose between a set of predefined models. (2) Speed. Automatic differentiation can make the difference between waiting hours and seconds for a converging model fit. (3) Precision. Automatic differentiation calculates the derivatives as accurately as if the analytical derivatives were implemented. (4) Quantification of uncertainties. With almost no extra effort AD Model builder produces several different estimates of the uncertainties of model parameters and selected derived quantities. A beginnersí course in ADMB likely will include: (1) An overview of ADMB. (2) A refresher on model development and likelihood based inference. (3) Installation and set up of the software. (4) First example. (5) Options for importing data (the simple and the more exotic). (6) Definition of model parameters (limits, phases, and some tricks). (7) Programming the likelihood function. (8) Specification and formatting of output. (9) Debugging, memory management, and other important implementation issues. (10) Estimation uncertainties (delta, profile, and MCMC methods). (11) Random effects models in AD Model Builder. The actual contents of the course will be customized to fit the audience. The format will be a mixture between lectures and hands-on exercises. Laptop required.